EP0380720A1 - Méthode de traitement d'image - Google Patents

Méthode de traitement d'image Download PDF

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Publication number
EP0380720A1
EP0380720A1 EP19890101564 EP89101564A EP0380720A1 EP 0380720 A1 EP0380720 A1 EP 0380720A1 EP 19890101564 EP19890101564 EP 19890101564 EP 89101564 A EP89101564 A EP 89101564A EP 0380720 A1 EP0380720 A1 EP 0380720A1
Authority
EP
European Patent Office
Prior art keywords
image processing
processing method
grouping
convolution
pattern
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
EP19890101564
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German (de)
English (en)
Other versions
EP0380720B1 (fr
Inventor
Ryohei C/O Ezel Inc. Kumagai
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Yozan Inc
Sharp Corp
Original Assignee
Yozan Inc
Ezel Inc
Sharp Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority to US07/302,350 priority Critical patent/US5151794A/en
Application filed by Yozan Inc, Ezel Inc, Sharp Corp filed Critical Yozan Inc
Priority to EP89101564A priority patent/EP0380720B1/fr
Priority to DE68928779T priority patent/DE68928779T2/de
Publication of EP0380720A1 publication Critical patent/EP0380720A1/fr
Application granted granted Critical
Publication of EP0380720B1 publication Critical patent/EP0380720B1/fr
Anticipated expiration legal-status Critical
Expired - Lifetime legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/20—Image enhancement or restoration using local operators
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03H—IMPEDANCE NETWORKS, e.g. RESONANT CIRCUITS; RESONATORS
    • H03H17/00—Networks using digital techniques
    • H03H17/02—Frequency selective networks
    • H03H17/0248—Filters characterised by a particular frequency response or filtering method
    • H03H17/0264—Filter sets with mutual related characteristics
    • H03H17/0266—Filter banks

Definitions

  • the present invention relates to an image processing method, particularly for smoothing without dulling edge.
  • a local averaging method for smoothing is usually applied, because this method can be easily executed by a software of a local spatial calculation.
  • an image is processed for the above smoothing in 32msec.
  • the local averaging method dulls edges of a configuration and make the image unclear as well as of low resolution.
  • various smoothing methods without dulling edge are proposed. Nagao method, local selecting method of pixels, hysteresis smoothing method, median filter method and E-filter method in frequency space.
  • median filter method is well known because of its high quality of the process result.
  • the smoothing methods without dulling edge, including median filter method need long process time and are difficult to be executed by a hardware. These methods cannot be applied to processes that is to be processed in high process speed.
  • the present invention has an object to provide an image processing method for smoothing without dulling edge.
  • predetermined size of convolution areas are successively read out, pixels in each convolution area are divided into a plurality of groups, the median value of each group is calculated, the median value of the median values of the groups is calculated and the last median value is defined as a representative value of the convolution.
  • Figs 1 (1) and (2) 3x3 convolutions to be read out are shown, in which references a, b, and c are given to pixels. These references represent groups for separating pixels. Each group includes 3 pixels vertically or horizontally aligned.
  • Figs 1 to 5 All types of groupings are shown in Figs 1 to 5.
  • the grouping in Fig. 1 is the most simple type.
  • N1 (1/2)n(n-1)2 (2) where, n is the number of groups as well as number of pixels in each group, "3" in this case.
  • N1 is smaller than N2/n, so the process time of the first embodiment has much higher process speed than the conventional median filter.
  • the first embodiment has a process speed 6 times of that of the conventional median filter.
  • Figs 8 to 13 show printer outputs of a process result by the embodiment and by the conventional median filter.
  • Fig. 8 shows a process result for an image without noises.
  • each dot is represented by " @ ", the original image is (A), the process result by the conventional median filter is (B) and the process result by the embodiment is (C).
  • "%" represents a pixel generated by the processing but not included in the original image.
  • the number of pixels in (B) is 13, while the number in (C) is 1. From the number, the processing of this embodiment is much better in the quality of the process result than the conventional median filter.
  • a ratio of the number of correct, or not changed, pixels divided by total pixel number of original image without noises is an index for evaluating the quality of the process result. This ratio is now called "preservation ratio".
  • the preservation ratio of the embodiment is 99.9%, while the ratio of the conventional median filter is 99.3%.
  • the images to be processed (A) in Figs 9 to 11 include noises of 5%
  • the image to be processed (A) in Figs 12 and 13 include noises of 10%.
  • Table 1 Image to be processed Noise % Process result of the embodiment Process result of the conventional median filter Error pixels Preservation ratio(%) Error pixels Preservation ratio(%) Fig. 8 0 1 99.9 13 99.3 Fig. 9 5 18 99.0 30 98.2 Fig. 10 5 18 99.0 28 98.4 Fig. 11 5 22 98.7 39 97.8 Fig. 12 10 50 97.1 54 96.9 Fig. 13 10 53 97.0 53 97.0
  • the preservation ratio represents the elimination quality as well, because the preservation ratio is identical with the identification ratio of the processed image to the original image without noises.
  • the reason why the processing according to the present invention has high quality is that the processing evaluates pixels in more macro point of view than the conventional median filter.
  • the center pixel at the right angle convex corner is deleted in the conventional median filter.
  • a center pixel is added on the right angle concave corner in the conventional median filter. This is a problem of the conventional median filter in preserving the original image.
  • Fig. 2 shows the second grouping of the first embodiment. By this grouping, the process result is similar to that of the first grouping.
  • Figs 7 (1) to (3) dot patterns are shown, which are processed to be higher quality image by the second grouping than the first grouping.
  • Figs 7(4) to (13) there are shown patterns occasionally better in the process result of the second grouping than the first.
  • the patterns rotationally or axially symmetric to the patterns in Figs 7 (4) to (13) are the same.
  • Figs 7 (1), (6) and (7) are patterns corresponding to an acute concavity with 45 degree. Such configuration is hardly exist in a natural image.
  • the corner point in the concavity can be deemed to be a pixel accidentally deleted.
  • the processing according to the second grouping can fill the corner point with a pixel of configuration's density.
  • Fig. 7 (2) shows a T-shaped pattern.
  • the center pixel of the vertical line of the T-shape should not be deleted.
  • the corresponding pixel should not be filled with configuration's density.
  • the processing according to the second grouping generates the process result of the T-shaped pattern meeting to the above requirement.
  • the rotationally or axially symmetric patterns are processed the same.
  • Fig. 7 (3) shows a rotated and reversed L-shaped pattern.
  • the center pixel of the vertical line of the T-­shape should not be filled with configuration's density.
  • the center pixel should not be deleted.
  • the center pixel in the reversed pattern is filled with configuration's density. While in the first embodiment according to the grouping of Figs 2 (1) to (7), (9) to (11), (15) to (17) and (20) to (23), the process result meets the above requirement.
  • Fig. 7 (4) shows a reversed steplike pattern.
  • the pixel on the step corner is filled with configuration's density, in the processing according to the first grouping. While the processing according to the grouping in Figs 2 (12) to (14), (18) to (20), (24) and (26) processes the pattern so as not to fill the pixel on the step corner.
  • the not reversed pattern, rotational symmetric pattern of reversed or not reversed pattern, and axially symmetric pattern of reversed or not reversed pattern are adequately processed by the grouping above, as well.
  • Fig. 7 (5) to (7) show a low trapezoid line pattern.
  • the pixel inside of the trapezoid should be filled with configuration's density.
  • the reversed pattern the inside of the trapezoid should be deleted.
  • the inside pixel is kept as it is.
  • the inside pixel is changed, meeting to the above requirement.
  • the reversed pattern, the rotationally symmetric pattern of the reversed or not reversed pattern, and the axially symmetric pattern of the reversed or not reversed pattern are adequately processed, as well.
  • Fig. 7 (8) shows a reversed Y-shaped pattern.
  • the cross point of Y-shape should not be filled with configuration's density.
  • the conventional median filter and the processing according to the first grouping fill the above cross point.
  • the processing according the the second grouping keeps the cross point as it is.
  • Fig. 7 shows a rotated and reversed short foot L-­shaped pattern with an additional dot.
  • the center pixel of the vertical line of the L-shape should not be filled with configuration's density. However, in the processing according to the first grouping, the center pixel is filled with configuration's density. While, according to the second grouping, the center pixel is not filled.
  • Fig. 7 shows a reversed high step-like pattern.
  • the middle pixel of the step should not be filled with configuration's density. However, according to the first grouping, the middle pixel is filled. While, according to the second grouping, the middle pixel is not filled.
  • Fig. 7 (11) shows a rotated and reversed low T-shaped pattern.
  • the center pixel of the horizontal line of T-shape should not be filled with configuration's density. However, according to the first grouping, the center pixel is filled. While, according to the second grouping the center pixel is not filled.
  • Fig. 7 (12) shows a reversed box with one dot hole.
  • the center pixel corresponding to the hole should be deleted so as to make the box holeless.
  • the conventional median filter and the processing according to the first grouping keep the center pixel as it is.
  • the center pixel is deleted.
  • the reversed pattern of the pattern in Fig. 7 (12), rotationally symmetric pattern of reversed or not reversed pattern and axially symmetric pattern of reversed or not reversed pattern are adequately processed, as well.
  • Fig. 7 (13) shows a crossing pattern.
  • the center pixel of the crossing should not be deleted. However, the center pixel is deleted, according to the first grouping. While the center pixel is not deleted according to the grouping in Figs 2(1), (3), (4), (6), (7), (9) to (12), (14), (19), (21) to (24) and (26).
  • the second grouping has process results 88% similar to that of the first grouping. Particularly the grouping in Figs 2 (2), (4), (5), (8), (13), (15), (17), (18), (20), (25) and (27) include a lot of better process results than that of the first grouping.
  • the process result of the third grouping (Fig. 3), the fourth grouping (Fig. 4) and the fifth grouping are identical in 86% pattern, 83% pattern and 81% pattern, respectively, with the process result of the first grouping whose high quality has already proved.
  • the second to the fifth grouping have process result 90% identical with the conventional median filter.
  • a 3x3 convolution is shown with references a to i in the order along the scan line.
  • the above calculations (2) and (3) mean a median calculation of a horizontal and vertical crossing pattern. This calculation is much simpler and takes much shorter calculation time than the first embodiment.
  • the calculation represents the total characteristics of the convolution.
  • the crossing may be changed to be oblique crossing.
  • the calculation for oblique crossing is as follows; Med ⁇ a, Med ⁇ c, e, g ⁇ , i ⁇ (6) or Med ⁇ c, Med ⁇ a, e, i ⁇ , g ⁇ (7)
  • Fig. 15 shows a 5x5 convolution in which references a to y in the order along the scan line are given.
  • the crossing pattern median calculation can be applied to the 5x5 convolution, as well.
  • the calculation is performed as follows; Med ⁇ k, l, Med ⁇ c, h, m, r, w ⁇ , n, o ⁇ (8) or Med ⁇ c, h, Med ⁇ k, l, m, n, o ⁇ , r, w ⁇ (9) or Med ⁇ e, i, Med ⁇ a, g, m, s, y ⁇ , q, u ⁇ (10) or Med ⁇ a, g, Med ⁇ e, i, m, q, u ⁇ , s, y ⁇ (11)
  • Table 2 shows the process result of the formula (2), (3), (6), (7) and the conventional median filter, with respect to the pattern in Figs 16 (a) to (j) and Figs 17 (a) to (1).
  • Table 2 Pattern Formula (2) Formula (3) Formula (6) Formula (7) Conventional Median Filter Fig.16 (a) 0 0 0 0 0 0 (b) 0 0 0 0 0 (c) 0 1 0 0 0 (d) 0 0 0 0 0 (e) 1 1 0 0 0 (f) 0 1 0 0 0 0 (g) 1 1 0 0 1 (h) 0 0 1 1 0 (i) 1 1 0 0 1 (j) 1 1 1 1 1 1 Fig.17 (a) 1 1 1 1 1 (b) 1 1 1 1 1 (c) 1 1 1 1 1 1 (d) 1 1 1 1 1 1 (e) 1 1 1 1 1 (f) 1 1 0 0 0 0
  • the patterns in Fig. 16 are processed by the formula (2), (3), (6) and (7) similar to the conventional median filter. With respect to the patterns in Fig. 17, the calculation results of the formulas have advantages that one-dot lines crossing the convolution are usually preserved.

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  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Computer Hardware Design (AREA)
  • Mathematical Physics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Image Processing (AREA)
EP89101564A 1989-01-30 1989-01-30 Méthode de traitement d'image Expired - Lifetime EP0380720B1 (fr)

Priority Applications (3)

Application Number Priority Date Filing Date Title
US07/302,350 US5151794A (en) 1989-01-30 1989-01-27 Image processing method
EP89101564A EP0380720B1 (fr) 1989-01-30 1989-01-30 Méthode de traitement d'image
DE68928779T DE68928779T2 (de) 1989-01-30 1989-01-30 Verfahren zur Bildverarbeitung

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
EP89101564A EP0380720B1 (fr) 1989-01-30 1989-01-30 Méthode de traitement d'image

Publications (2)

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EP0380720A1 true EP0380720A1 (fr) 1990-08-08
EP0380720B1 EP0380720B1 (fr) 1998-08-12

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EP89101564A Expired - Lifetime EP0380720B1 (fr) 1989-01-30 1989-01-30 Méthode de traitement d'image

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US (1) US5151794A (fr)
EP (1) EP0380720B1 (fr)
DE (1) DE68928779T2 (fr)

Cited By (1)

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Publication number Priority date Publication date Assignee Title
EP0819244A1 (fr) * 1995-04-04 1998-01-21 Bacharach, Inc. Systeme de representation visuelle de gaz

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US6336180B1 (en) 1997-04-30 2002-01-01 Canon Kabushiki Kaisha Method, apparatus and system for managing virtual memory with virtual-physical mapping
DE68923412T2 (de) * 1988-08-26 1995-12-21 Canon Kk Bildverarbeitungseinrichtung.
US5404233A (en) * 1990-08-28 1995-04-04 Kyocera Corporation Method for smoothing image
US5424783A (en) * 1993-02-10 1995-06-13 Wong; Yiu-Fai Clustering filter method for noise filtering, scale-space filtering and image processing
JPH06291974A (ja) * 1993-03-31 1994-10-18 Toshiba Corp ファクシミリ装置
US5940190A (en) * 1993-08-23 1999-08-17 Lexmark International, Inc. Image improvement after facsimile reception
US5592571A (en) * 1994-03-08 1997-01-07 The University Of Connecticut Digital pixel-accurate intensity processing method for image information enhancement
AU2543095A (en) * 1994-03-08 1995-09-25 University Of Connecticut, The Digital pixel-accurate intensity processing method for image information enhancement
US5563962A (en) * 1994-03-08 1996-10-08 The University Of Connecticut Two dimensional digital hysteresis filter for smoothing digital images
AUPO648397A0 (en) 1997-04-30 1997-05-22 Canon Information Systems Research Australia Pty Ltd Improvements in multiprocessor architecture operation
US6311258B1 (en) 1997-04-03 2001-10-30 Canon Kabushiki Kaisha Data buffer apparatus and method for storing graphical data using data encoders and decoders
US6349379B2 (en) 1997-04-30 2002-02-19 Canon Kabushiki Kaisha System for executing instructions having flag for indicating direct or indirect specification of a length of operand data
US6707463B1 (en) 1997-04-30 2004-03-16 Canon Kabushiki Kaisha Data normalization technique
US6674536B2 (en) 1997-04-30 2004-01-06 Canon Kabushiki Kaisha Multi-instruction stream processor
AUPO647997A0 (en) 1997-04-30 1997-05-22 Canon Information Systems Research Australia Pty Ltd Memory controller architecture
US6546148B1 (en) * 1997-07-14 2003-04-08 California Institute Of Technology Circuitry for determining median of image portions
US8885945B2 (en) 2012-12-27 2014-11-11 Mitutoyo Corporation Method for improving repeatability in edge location results of a machine vision inspection system

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0819244A1 (fr) * 1995-04-04 1998-01-21 Bacharach, Inc. Systeme de representation visuelle de gaz
EP0819244A4 (fr) * 1995-04-04 1999-04-14 Bacharach Inc Systeme de representation visuelle de gaz

Also Published As

Publication number Publication date
EP0380720B1 (fr) 1998-08-12
DE68928779T2 (de) 1998-12-24
DE68928779D1 (de) 1998-09-17
US5151794A (en) 1992-09-29

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